Abstract: Systems and methods are provided for generating a map for use in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, aerial image data depicting an environment; processing, by the processor, the aerial image data with a plurality of trained deep learning models to produce a predicted radar map; and controlling the vehicle based on the predicted radar map.

Abstract: A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

Abstract: Systems and methods are provided for interpreting traffic information by a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensing devices of the vehicle, where the sensor data depicts a traffic device in an environment of the vehicle; receiving, by the processor, map data associated with the environment of the vehicle, where the map data includes traffic devices; matching, by the processor, the traffic device of the sensor data with a traffic device of the map data; determining, by the processor, a probability distribution of a traffic signal associated with the matched traffic devices based on a Hidden Markov model (HMM); and planning, by the processor, control of the vehicle based on the probability distribution.

Abstract: Systems and methods to generate an augmented map used for autonomous driving of a vehicle involve obtaining images at a first point of view, and training a first neural network to identify and classify features related to an attribute in the images at the first point of view. A method also includes projecting the features onto images obtained at a second point of view, and training a second neural network to identify the attribute in the images at the second point of view based on the features. The augmented map is generated by adding the attribute to a map image at the second point of view.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method for generating map features includes receiving, by a processing device, aerial image data. The method further includes receiving, by the processing device, telemetry data. The method further includes performing, by the processing device, data fusion on the aerial image data and the telemetry data to generate map features. The method further includes controlling, by the processing system, a vehicle based at least in part on the map features.

Abstract: A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

Abstract: Systems and methods are provided for generating a map for use in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, aerial image data depicting an environment; processing, by the processor, the aerial image data with a plurality of trained deep learning models to produce a predicted radar map; and controlling the vehicle based on the predicted radar map.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, landmark data obtained from an image sensor of the vehicle; fusing, by the processor, the landmark data with vehicle pose data to produce fused lane data, wherein the fusing is based on a Kalman filter; retrieving, by the processor, map data from a lane map based on the vehicle pose data; selectively updating, by the processor, the lane map based on a change in the fused lane data from the map data; and controlling, by the processor, the vehicle based on the updated lane map.

Abstract: Systems and methods are provided for interpreting traffic information by a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensing devices of the vehicle, where the sensor data depicts a traffic device in an environment of the vehicle; receiving, by the processor, map data associated with the environment of the vehicle, where the map data includes traffic devices; matching, by the processor, the traffic device of the sensor data with a traffic device of the map data; determining, by the processor, a probability distribution of a traffic signal associated with the matched traffic devices based on a Hidden Markov model (HMM); and planning, by the processor, control of the vehicle based on the probability distribution.

Abstract: A method for autonomous vehicle map construction includes automatically capturing location data, movement data, and perception data from a vehicle that has traveled down a road, wherein the perception data includes data that identifies the location of lane edges and lane markers for the road, the location of traffic signs associated with the road, and the location of traffic signaling devices for the road. The method further includes pre-processing to associate the captured perception data with the captured location data, captured movement data, and navigation map data; determining, from the pre-processed data, lane boundary data, traffic device and sign location data, and lane level intersection data that connects the intersecting and adjoining lanes identified through the lane boundary data; and storing the lane boundary data, traffic device and sign location data, and lane level intersection data in a map file configured for use by an autonomous vehicle.

Abstract: A system and method for providing a map via a cloud-based system is disclosed. The method includes requesting, by a controller of an autonomous vehicle, a map data from a cloud-based server. The map data is stored on the cloud-based server. The method also includes receiving map data from the cloud-based server. The method also includes autonomously navigating the autonomous vehicle based on the received map data.

Abstract: A system and method for providing a map via a cloud-based system is disclosed. The method includes requesting, by a controller of an autonomous vehicle, a map data from a cloud-based server. The map data is stored on the cloud-based server. The method also includes receiving map data from the cloud-based server. The method also includes autonomously navigating the autonomous vehicle based on the received map data.

Abstract: A method for vehicle-to-vehicle communication is disclosed. The method includes establishing a communication connection between the first vehicle and the second vehicle, receiving one or more communications from the second vehicle, determining, by the first vehicle, vehicle parameter targets based on the one or more communications, communicating the vehicle parameter targets and one or more vehicle device commands to the second vehicle, and determining at least one vehicle control signal for the second vehicle based on the vehicle parameter targets and the one or more vehicle device commands.

Abstract: Methods and apparatus are provided for controlling a vehicle. The apparatus includes an operator monitoring system configured to monitor an operator of the vehicle for a temporary incapacity, and an autonomous driving system configured to operate the vehicle in a temporary autonomous operation mode. A manual driving system is provided to operate the vehicle in a manual mode and a mode determination system is provided to transition a current mode of the vehicle between the temporary autonomous operation mode and the manual mode based on the temporary incapacity.

Abstract: A method for vehicle-to-vehicle communication is disclosed. The method includes establishing a communication connection between the first vehicle and the second vehicle, receiving one or more communications from the second vehicle, determining, by the first vehicle, vehicle parameter targets based on the one or more communications, communicating the vehicle parameter targets and one or more vehicle device commands to the second vehicle, and determining at least one vehicle control signal for the second vehicle based on the vehicle parameter targets and the one or more vehicle device commands.

Abstract: A system and method for providing lane changing maneuvers in an autonomously driven vehicle. The vehicle includes a navigation controller that provides a planned route for the vehicle to follow and a vehicle controller that receives route information from the navigation controller and provides steering, braking and throttle control for the vehicle to follow the route. Either the navigation controller or the vehicle controller may initiate a lane change maneuver to cause the vehicle to be steered from a travel lane to an adjacent lane. In response to the lane change requirement, the navigation controller provides a route segment to the vehicle controller and a lane-change zone so that the vehicle controller can steer the vehicle to the adjacent lane while in the lane-change zone.

Abstract: A method for prioritizing potential threats identified by vehicle active safety systems. The method includes providing context information including map information, vehicle position information, traffic assessment information, road condition information, weather condition information, and vehicle state information. The method calculates a system context value for each active safety system using the context information. Each active safety system provides a system threat level value, a system braking value, a system steering value, and a system throttle value. The method calculates an overall threat level value using all of the system context values and all of the system threat level values. The method then provides a braking request value to vehicle brakes based on all of the system braking values, a throttle request value to a vehicle throttle based on all of the system throttle values, and a steering request value to vehicle steering based on all of the system steering values.

Abstract: Methods and systems for dynamically prioritizing target areas to monitor around a vehicle are provided. The system, for example, may include, but is not limited to a sensor, a global positioning system receiver, and a processor communicatively coupled to the sensor and the global positioning system receiver. The processor is configured to determine a location of the vehicle and based upon data from the global positioning system receiver, determine a projected path the vehicle is traveling upon, prioritize target areas based upon the determined location, heading and the projected path, and analyze data from the sensor based upon the prioritized target areas.

Abstract: A method for prioritizing potential threats identified by vehicle active safety systems. The method includes providing context information including map information, vehicle position information, traffic assessment information, road condition information, weather condition information, and vehicle state information. The method calculates a system context value for each active safety system using the context information. Each active safety system provides a system threat level value, a system braking value, a system steering value, and a system throttle value. The method calculates an overall threat level value using all of the system context values and all of the system threat level values. The method then provides a braking request value to vehicle brakes based on all of the system braking values, a throttle request value to a vehicle throttle based on all of the system throttle values, and a steering request value to vehicle steering based on all of the system steering values.

Abstract: A system and method for providing lane changing maneuvers in an autonomously driven vehicle. The vehicle includes a navigation controller that provides a planned route for the vehicle to follow and a vehicle controller that receives route information from the navigation controller and provides steering, braking and throttle control for the vehicle to follow the route. Either the navigation controller or the vehicle controller may initiate a lane change maneuver to cause the vehicle to be steered from a travel lane to an adjacent lane. In response to the lane change requirement, the navigation controller provides a route segment to the vehicle controller and a lane-change zone so that the vehicle controller can steer the vehicle to the adjacent lane while in the lane-change zone.